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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Makhazhanova, Ulzhan
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Computer Science & IT Electrical & Electronics Engineering

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Forecasting creditworthiness in credit scoring using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Amirov, Azamat; Tassuov, Bolat; Makhatova, Valentina; Kaipova, Assemgul; Makhazhanova, Ulzhan; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5534-5542

Abstract

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Application of machine learning methods to analysis and evaluation of distance education Mukhiyadin, Ainur; Mukasheva, Manargul; Makhazhanova, Ulzhan; Kassekeyeva, Aislu; Azieva, Gulmira; Kenzhebayeva, Zhanat; Abdrakhmanova, Alfiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2172-2180

Abstract

In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.
Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods Kassymova, Akmaral; Aibatullin, Tolegen; Yelezhanova, Shynar; Konyrkhanova, Assem; Mukhanbetkaliyeva, Ainur; Tynykulova, Assemgul; Makhazhanova, Ulzhan; Azieva, Gulmira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10458

Abstract

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
Co-Authors Abdrakhmanova, Alfiya Aibatullin, Tolegen Amirov, Azamat Azieva, Gulmira Baitemirov, Madiyar Kaipova, Assemgul Kassekeyeva, Aislu Kassymova, Akmaral Kenzhebayeva, Zhanat Konyrkhanova, Assem Makhatova, Valentina Mukasheva, Manargul Mukhanbetkaliyeva, Ainur Mukhanova, Ayagoz Mukhiyadin, Ainur Ospanova, Tleugaisha Tassuov, Bolat Tynykulova, Assemgul Yelezhanova, Shynar